Event-based approaches. Real-time applications require a careful design to meet strong computational, speed, and energy requirements. This premise is especially true when fast-moving human subjects are involved, such as in sport assistance and virtual reality. Monocular solutions involving RGB-D sensors [1]} and high-speed cameras [2]} have been explored, although they cannot meet the computational requirements of real-time applications. On the other hand, event-cameras achieve high recording speed without saturating bandwidth and resources.
For these reasons, human pose estimation performed with event-cameras is both interesting and challenging for the community. Approaches that extrapolate information from single events would be ideal, as these methods allow to exploit the interesting advantages of event-cameras. Initial proposals leveraged event-cameras to match events with known objects in the scene [3]}, [4]}. Rebecq exploit a similar caveat [5]} to predict semi-dense 3D structure of a scene. More recently, Xu [6]} employ events to (1) track features across frames and (2) enhance the intensity outputs of a DAVIS camera. Next, they predict human-poses with VNect [7]} and Openpose [8]} models and optimize a multi-step optimization scheme to refine the prediction. Despite its efficiency, their approach relies on a heavy pre-processing phase to extract 3D mesh of the subjects and involves multiple components, each with its own hyper-parameters.
Instead of processing events in small batches, numerous works accumulate events into tensors representations, conducing events in the realm of synchronous deep learning models [9]}. To predict human poses through event-cameras, previous works aggregate events to predict 2D poses from multiple point of views and finally triangulate subjects' 3D poses [10]}, [11]}.
On the other hand, our approach is the first attempt to estimate 3D human pose based on a single DVS camera. We prove that human pose estimation from event-only DVS camera is feasible. For an in-depth discussion of event-cameras and their applications, we refer to the excellent event-cameras summary [9]}.
